Partial least squares

As noted before (Section 4.5.3), this study employs PLS-SEM to assess the adequacy and validity of measurement models, examine the predictive relevance of the synchronised multi- level - multi-unit ambidexterity framework (Figure 3.1), and test the eight hypotheses. PLS- SEM allows simultaneous examination of measures and hypotheses (i.e., the outer and inner models) (Chin et al., 2003; Hair et al., 2011b). In particular, the PLS-SEM estimation process continuously oscillates between estimating case values for block variables and model parameters that depend on these case values (Reinartz et al., 2009). Block variables are the weighted average of all items that belong to the same construct. As all block variables are linear combinations of their items, PLS-SEM does not suffer from improper solutions and factor indeterminacy which sometimes occurs when using CB-SEM techniques (Reinartz et al., 2009). By continuously oscillating between estimating case values for the block variables and model parameters PLS-SEM allows simultaneous analysis of (1) how well the measures relate to each construct and (2) whether the hypothesised relationships at the theoretical level are empirically true (Limayem, Hirt, & Cheung, 2007; Reinartz et al., 2009; Hair et al., 2011b).

PLS-SEM has for some time now been increasingly applied in the marketing and management literature to test the theory (e.g., Bontis & Booker, 2007; Cording et al., 2008; Henseler, Ringle, & Sinkovics, 2009; Ngo & O'Cass, 2009; Akgun et al., 2010; De Luca et al., 2010; Nakata et al., 2010; Navarro et al., 2010; Brettel et al., 2011; Hair et al., 2011a; Kumar, Heide, & Wathne, 2011; Slotegraaf & Atuahene-Gima, 2011; O'Cass, Ngo, & Heirati, 2012). PLS-SEM has been used to overcome some of the identified limitations of covariance-based (CB-SEM) techniques (i.e., employed via software such LISREL and Amos) (Wold, 1985; Hulland, 1999; Chin et al., 2003; Reinartz et al., 2009; Hair et al.,

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2011b). The consideration given to use PLS-SEM over CB-SEM techniques is premised on several benefits.

First, PLS-SEM is distribution free approach (Hulland, 1999; Hair et al., 2011a; Kumar et al., 2011). Specifically, PLS-SEM is recommended in the presence of three conditions: (1) high skewness (beyond the range of ± 2) instead of symmetric distribution for items (or manifest variables), (2) multicollinearity within blocks of manifest and between constructs (or latent variables), and (3) misspecified measurement models and incorrect structural relations (Cassel, Hackl, & Westlund, 1999). Second, PLS-SEM focuses on maximising the variance explained for all endogenous constructs in a model, whereas CE-SEM techniques determine model parameters to reproduce an empirically observed covariance matrix (Reinartz et al., 2009; Hair et al., 2011a). Therefore, PLS-SEM is more advantageous for predictive research rather than confirmatory studies (Fornell & Bookstein, 1982; Wold, 1985; Hair et al., 2011b). In addition, PLS-SEM is more advantageous than covariance based approaches when measures are not well established (Fornell & Bookstein, 1982; Slotegraaf & Atuahene-Gima, 2011). Third, some CB-SEM discrepancy indices (i.e., GFI and AGFI) decline as model complexity increases (i.e. more items or more constructs), and they may be inappropriate for more complex models (Henseler et al., 2009; Hair et al., 2011b). In contrast, PLS-SEM path models can be very complex without leading to estimation problems (Fornell & Bookstein, 1982; Wold, 1985; Chin & Newsted, 1999; Henseler et al., 2009; Reinartz et al., 2009; Hair et al., 2011a; Hair et al., 2011b). Fourth, since PLS-SEM has minimum demands regarding sample size to achieve acceptable levels of statistical power (Reinartz et al., 2009; Hair et al., 2011b). Conversely, CB-SEM involves constraints regarding the number of observations and small sample sizes, often lead to biased test statistics, inadmissible solutions, and identification problems (Chin & Newsted, 1999; Henseler et al., 2009). According to the rule of thumb suggested by Barclay et al. (1995), the minimum

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sample size for PLS-SEM is equal to the larger of the following: (1) ten times the number of items of the scale with the largest number of formative items in the outer-measurement model, or (2) ten times the largest number of structural paths directed at a particular construct in the inner-structural model (see also Henseler et al., 2009).

Based on the outlined benefits of PLS-SEM, this study employed PLS-SEM, specifically Smart-PLS (Ringle, Wende, & Will, 2005), for several reasons. First, as noted in section 5.2, a number of the measurement item distributions departed from normal distribution. Second, the primary purpose of this study is to predict the extent that the synchronous pursuit of ambidexterity at both corporate and business levels drives new product performance, established product performance, and ultimate firm performance. Third, this study developed ostensibly new measures for some constructs of interest such as exploratory strategy, exploitative strategy, exploratory R&D, exploitative R&D, exploratory marketing, and exploitative marketing. Fourth, given the number of constructs and items (18 constructs and 103 items including control variables) and indirect effects among constructs within the synchronised multi-level - multi-unit ambidexterity framework, this theoretical framework is considered as complex. Fifth, based on Barclay et al.’s (1995) rule of thumb, 120 cases is the minimum sample size for this study, which is less than the final number of completed survey packages obtained (169). Therefore, this study achieved a satisfactory number of respondents to utilise PLS-SEM. Finally, PLS-SEM has been used extensively in analysing interaction effects (Chin et al., 2003; Slotegraaf & Dickson, 2004; Slotegraaf & Atuahene-Gima, 2011) and mediational effects (Bontis & Booker, 2007; Giebelhausen, Robinson, & Cronin, 2010; Sattler, Völckner, Riediger, & Ringle, 2010; Ngo & O'Cass, 2012; Siren et al., 2012). Given, mediational effects of business-level capabilities (hypotheses 1 to 4), operational ambidexterity (hypotheses 5 and 6), and product performance (hypotheses 7 and 8) depicted in Figure 3.1, PLS-SEM is appropriate for this study.

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The PLS-SEM analysis typically follows a two-step processes including separate assessment of two models, namely outer-measurement model and the inner-structural model (Cassel et al., 1999; Henseler et al., 2009; Hair et al., 2011b). The outer-measurement model includes the unidirectional predictive relationships between each construct and its respective items (Figure 5.1) (Cassel et al., 1999). In a structural model, multiple relations are not permitted and each item should be only associated with a single construct (Hair et al., 2011b). The inner-structural model shows the relationships (paths) between the constructs. As shown in Figure 5.1, constructs in the structural model are classified as exogenous and endogenous variables (Hair et al., 2011b). Exogenous variables are constructs that do not have any structural path relationships pointing at them. Endogenous variables are constructs that are explained by other exogenous variables via structural model relationships (Jarvis, Mackenzie, & Podsakoff, 2003; Hair et al., 2011b). The assessments of outer- and inner-structural models for this study’s theoretical framework are detailed in Section 5.4 and 5.5.